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How to use joint plot method in seaborn

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by l.rohitharohitha2001@gmail.com dot icon Updated: Sep 7, 2023

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Seaborn is a statistical data visualization library. It has another popular visualization library for Python. It provides a high-level interface for creating pleasing and informative visualizations.


The joint plot function is one of Seaborn's tools for this purpose. Seaborn's Joint plot is a function designed to ease the visualization of variables. Developers created the Seaborn Library in the 2010s, not the early 20th century. Python uses it for creating data visualizations, not for landscape design.   

Uses of Joint Plot:  

  1. Jointplot: Jointplot is a tool. It analyzes data in different fields, like business, finance, and healthcare. It helps explore relationships, distributions, and correlations between variables.  
  2. Epidemiology and Public Health: It helps study the interconnectedness of health factors. Environmental factors can help us understand disease rates.  
  3. Environmental scientists: Scientists can study the relationship between temperature, precipitation, and biodiversity. This can aid in ecosystem analysis and conservation efforts.  
  4. Geographical Information Systems (GIS): GIS helps visualize and understand spatial data. Jointplot helps analyze land use, population distribution, and natural resources.  
  5. Landscape Design: Landscape designers use jointplot to analyze elements, terrain, and ecology. It helps in creating sustainable and pleasing landscapes.  
  6. Urban planning and design: It studies how different parts of a city connect and grow. A jointplot helps analyze these relationships. These factors include population density, transportation, and infrastructure. This aids in making informed decisions about city planning and design.  
  7. Climate scientists: It analyzes climate data and relationships between variables using a jointplot. They analyze temperature, precipitation, and greenhouse gas concentrations. This assists in climate modeling and prediction.  
  8. Agriculture and crop science: It helps study how soil, weather, and water affect crops. It aids in optimizing agricultural practices and crop selection.  
  9. Architecture and Building Design: These designers can use jointplot to explore relationships. They can study the connections between design parameters, energy efficiency, and environmental impact. This helps in creating sustainable and efficient structures.  

  

To sum up, it's important to highlight the significance of using Seaborn's joint plot. Rather than using it differently, it assists with visualizing and analyzing data. The joint plot function within the Seaborn library serves as a powerful tool. The relationships between variables in a dataset enable researchers, analysts, and data scientists.   

Fig: Preview of the output that you will get on running this code from your IDE.

Code

In this solution we are using Seaborn library of Python.

How to make a jointplot in Seaborn with multiple groups or categories?

PythondotLines of Code : 26dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

col1 = [1, 1.5, 3.1, 3.4, 2, -1]
col2 = [1, -3, 2, 8, 2.5, -1.3]
col3 = [4, 3, 4, 0.5, 1, 0.3]
col4 = [10, 12, 10, 'NaN', 13, 'NaN']
col5 = ['A', 'A', 'A', 'B', 'A', 'B']
df = pd.DataFrame(list(zip(col1, col2, col3, col4, col5)),
                  columns=['x', 'y', 'z', 'alt_Z', 'cat'])
df['alt_Z'] = df['alt_Z'].astype(float)

ax = sns.scatterplot(data=df, x='x', y='alt_Z', color='black', label='alt_Z')
sns.scatterplot(data=df, x='x', y='z', hue='cat', ax=ax)

plt.show()

df1 = df[['x', 'z', 'cat']]
df2 = df[['x', 'alt_Z']].rename(columns={'alt_Z': 'z'}).dropna()
df2['cat'] = 'alt_Z'

g = sns.jointplot(data=df1.append(df2), x='x', y='z', hue='cat', palette={'alt_Z': 'black', 'A': 'orange', 'B': 'green'})
g.ax_joint.set_xlim(-3, 6) # the default limits are too wide for these reduced test data
plt.show()

Instructions


Follow the steps carefully to get the output easily.


  1. Download and Install the Jupyter Notebook on your computer.
  2. Open the terminal and install the required libraries with the following commands.
  3. Create a new Python file on your Notebook.
  4. Copy the snippet using the 'copy' button and paste it into your Python.
  5. Run the current file to generate the output.


I hope you found this useful.


I found this code snippet by searching for 'How to make joint plot in seaborn with multiple groups or categories' in Kandi. You can try any such use case!

Environment Tested


I tested this solution in the following versions. Be mindful of changes when working with other versions.

  1. Jupyter Notebook (anaconda 3) 6.0.1 Version
  2. The solution is created in Python 3.8 Version
  3. Seaborn 0.12.2 Version.


Using this solution, we can be able to use joint plot method in seaborn using Python with simple steps. This process also facilities an easy way to use, hassle-free method to create a hands-on working version of code which would help us to use joint plot method in seaborn using Python.

Dependent Library


seaborn-databy mwaskom

Python doticonstar image 1214 doticonVersion:Currentdoticon
no licences License: No License (null)

Data repository for seaborn examples

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            seaborn-databy mwaskom

            Python doticon star image 1214 doticonVersion:Currentdoticonno licences License: No License

            Data repository for seaborn examples
            Support
              Quality
                Security
                  License
                    Reuse

                      You can search for any dependent library on kandi like 'seaborn-data'.

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